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Humidity estimation of vehicle proton exchange membrane fuel cell under variable operating temperature based on adaptive sliding mode observation

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  • Jiao, Jieran
  • Chen, Fengxiang

Abstract

Active humidity control of vehicle fuel cell requires real-time and accurate estimation of actual humidity. However, the vehicle operation conditions change rapidly, which makes the accuracy of transient estimation become the difficulty of estimation algorithm with operation condition deviation. In order to solve this problem, an adaptive sliding mode estimation algorithm based on dimensionless modelling method is designed. The experiment on a real 80 kW commercial fuel cell system proves that compared with the classical sliding mode observation algorithm, the adaptive scheme can reduce the absolute estimation error of humidity by about 5% on average, and the absolute estimation error can be reduced by 17% in some transients.

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  • Jiao, Jieran & Chen, Fengxiang, 2022. "Humidity estimation of vehicle proton exchange membrane fuel cell under variable operating temperature based on adaptive sliding mode observation," Applied Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:appene:v:313:y:2022:i:c:s0306261922002288
    DOI: 10.1016/j.apenergy.2022.118779
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    References listed on IDEAS

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    1. Özçelep, Yasin & Sevgen, Selcuk & Samli, Ruya, 2020. "A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression," Renewable Energy, Elsevier, vol. 156(C), pages 570-578.
    2. Petrone, Giovanni & Zamboni, Walter & Spagnuolo, Giovanni, 2019. "An interval arithmetic-based method for parametric identification of a fuel cell equivalent circuit model," Applied Energy, Elsevier, vol. 242(C), pages 1226-1236.
    3. Hua, Zhiguang & Zheng, Zhixue & Péra, Marie-Cécile & Gao, Fei, 2020. "Remaining useful life prediction of PEMFC systems based on the multi-input echo state network," Applied Energy, Elsevier, vol. 265(C).
    4. Pei, Pucheng & Li, Yuehua & Xu, Huachi & Wu, Ziyao, 2016. "A review on water fault diagnosis of PEMFC associated with the pressure drop," Applied Energy, Elsevier, vol. 173(C), pages 366-385.
    5. Pan, Rui & Yang, Duo & Wang, Yujie & Chen, Zonghai, 2020. "Health degradation assessment of proton exchange membrane fuel cell based on an analytical equivalent circuit model," Energy, Elsevier, vol. 207(C).
    6. Lin, Rui & Tang, Shenghao & Diao, Xiaoyu & Zhong, Di & Chen, Liang & Froning, Dieter & Hao, Zhixian, 2020. "Detailed optimization of multiwall carbon nanotubes doped microporous layer in polymer electrolyte membrane fuel cells for enhanced performance," Applied Energy, Elsevier, vol. 274(C).
    7. Xing, Lei & Das, Prodip K. & Song, Xueguan & Mamlouk, Mohamed & Scott, Keith, 2015. "Numerical analysis of the optimum membrane/ionomer water content of PEMFCs: The interaction of Nafion® ionomer content and cathode relative humidity," Applied Energy, Elsevier, vol. 138(C), pages 242-257.
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    Citations

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    Cited by:

    1. Haibo Huo & Jiajie Chen & Ke Wang & Fang Wang & Guangzhe Jin & Fengxiang Chen, 2023. "State Estimation of Membrane Water Content of PEMFC Based on GA-BP Neural Network," Sustainability, MDPI, vol. 15(11), pages 1-16, June.
    2. Ruifeng Guo & Dongfang Chen & Yuehua Li & Wenlong Wu & Song Hu & Xiaoming Xu, 2023. "Anode Nitrogen Concentration Estimation Based on Voltage Variation Characteristics for Proton Exchange Membrane Fuel Cell Stacks," Energies, MDPI, vol. 16(5), pages 1-16, February.
    3. Chen, Fengxiang & Pei, Yaowang & Jiao, Jieran & Chi, Xuncheng & Hou, Zhongjun, 2023. "Energy flow and thermal voltage analysis of water-cooled PEMFC stack under normal operating conditions," Energy, Elsevier, vol. 275(C).
    4. Chi, Xuncheng & Chen, Fengxiang & Mo, Tiande & Li, Yu & Wei, Wei, 2024. "Improve methanol efficiency for direct methanol fuel cell system via investigation and control of optimal operating methanol concentration," Energy, Elsevier, vol. 290(C).
    5. Jiaping Xie & Hao Yuan & Yufeng Wu & Chao Wang & Xuezhe Wei & Haifeng Dai, 2023. "Impedance Acquisition of Proton Exchange Membrane Fuel Cell Using Deeper Learning Network," Energies, MDPI, vol. 16(14), pages 1-18, July.
    6. Pei, Yaowang & Chen, Fengxiang & Jiao, Jieran & Liu, Shiguang, 2024. "Analysis and control strategy design for PEMFC purging process," Energy, Elsevier, vol. 290(C).
    7. Chen, Fengxiang & Chi, Xuncheng & Wei, Wei & Mo, Tiande & Li, Yu, 2023. "Model-based observer for direct methanol fuel cell concentration estimation by using second-order sliding-mode algorithm," Energy, Elsevier, vol. 263(PD).

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